Using this approach for a six degree of freedom robotic arm needing 10 bit decoded outputs at 50 Hz, data rate comes to 3 kbps. This is approximately three orders of magnitude lower than transmitting the raw data.
Furthermore, the computational cost to include feature extraction block is \(\approx 40\)  nW per channel, which leads to a total of 4 \(\mu \) W power consumption for 100 electrodes. We propose ultra-low power neuromorphic Spike-input Extreme Learning Machine (SELMA) to be used as the decoder of choice since it yields \(\approx 10\)  times lower power consumption than fully tuned digital architectures [1]} without compromising on generalization capabilities [2]}.The power consumption comes to \(\approx \)  \(0.71\)  \(\mu \) W. Thus, combining both decoder and feature extraction blocks merely adds up to \(4.71\)  \(\mu \) W to the total power consumption. This is three orders of magnitude lesser than the pre-conditioner circuits typically consuming power in the mW range [3]}. To summarize, we have shown that incorporating feature extraction and neuromorphic decoder blocks in the implant leads to up to three orders of magnitude reduction in the power intensive data rate, while adding an almost negligible fraction to the existing power budget. Accordingly, we hope for a significant increase in the battery lifetime [4]}.
